Top 10 Python tools for machine learning and data science

It is no news that Python is one of the most popular languages out there and one of the reasons for this success is that it offers an extensive coverage for scientific computing. Here we take a closer look at the top 10 Python tools for machine learning and data science.

Experts have made it quite clear that 2018 will be a bright year for artificial intelligence and machine learning. Some of them have also expressed their opinion that “Machine learning tends to have a Python flavor because it’s more user-friendly than Java”.

When it comes to data science, Python’s syntax is the closest to the mathematical syntax and, therefore, is the language that is most easily understood and learned by professions like mathematicians or economists.

Here I will present my top 10 list of the most useful Python tools for both machine learning and data science applications. If you feel like deepening your knowledge in either field and you don’t know where to start, this is the best place for you! Take a look at the list and choose what suits you most!

Machine learning tools

Shogun – Shogun is an open-source machine learning toolbox with a focus on Support Vector Machines (SVM), it is written in C++ and it’s among the oldest machine learning tools, created in 1999! It offers a wide range of unified machine learning methods and the goal behind its creation is to provide machine learning with transparent and accessible algorithms as well as free machine learning tools to anyone interested in the field.

Shogun offers a well-documented Python interface and it is mostly designed for unified large-scale learning and offers a high-performance speed. However, some find its API difficult to use.

When Guido van Rossum developed Python, he wanted to create a “simple” programming language that bypassed the vulnerabilities of other systems. Due to the simple syntax and sophisticated syntactic phrases, the language has become the standard for various scientific applications such as machine learning.

Keras – Keras is a high-level neural networks API and provides a Python deep learning library. This is the best choice for any beginner in machine learning since it offers an easier way to express neural networks, compared to other libraries. Keras is written in Python and is capable of running on top of popular neural network frameworks like TensorFlow, CNTK or Theano.

According to the official site, Keras focuses on 4 main guiding principles that are user friendliness, modularity, easy extensibility and working with Python. However, when it comes to speed, Keras is at a disadvantage over other libraries.

Scikit-Learn – This is an open source tool for data mining and data analysis. Although it’s listed under machine learning in this article, it is suitable for uses in data science as well. Scikit-Learn provides a consistent and easy to use API as well as grid and random search. One of its main advantages is its speed in performing different benchmarks on toy datasets. Scikit-Learn’s main features include classification, regression, clustering, dimensionality reduction, model selection and preprocessing.

Pattern – Pattern is a web mining module and provides tools for data mining, natural language processing, machine learning, network analysis and <canvas> visualization. It also comes with well-documentation and more than 50 examples as well as over 350 unit tests. And most importantly, it’s free!

Theano – Arguably one of the most mature Python deep learning libraries, Theano is named after the Greek Pythagorean philosopher and mathematician who, allegedly, was the pupil, daughter or wife of Pythagoras. Theano’s main features include tight integration with NumPy, transparent use of GPU, efficient symbolic differentiation, speed and stability optimizations, dynamic C code generation and extensive unit-testing and self-verification.

Data science tools

SciPy – This is a Python-based ecosystem of open-source software for mathematics, science, and engineering. SciPy uses various packages like NumPy, IPython or Pandas to provide libraries for common math- and science-oriented programming tasks. This tool is a great option when you want to manipulate numbers on a computer and display or publish the results and it is free as well.

Dask – Dask is a tool providing parallelism for analytics by integrating into other community projects like NumPy, Pandas and Scikit-Learn. With this too, you can quickly parallelize existing code by changing only a few lines of code, since its DataFrame is the same as in the Pandas library, its Array object works like NumPy’s has the ability to parallelize jobs written in pure Python, as well.

Numba – This tool is an open source optimizing compiler that uses the LLVM compiler infrastructure to compile Python syntax to machine code. The main advantage of working with Numba in data science applications is its speed when using code with NumPy arrays since Numba is a NumPy aware compiler. Just like Scikit-Learn, Numba is also suitable for machine learning applications as its speedups can run even faster on hardware that is particularly built for either machine learning or data science applications.

Cython – When working with math-heavy code or code that runs in tight loops, Cython is your best choice. Cython is a source code translator based on Pyrex that allows you to easily write C extensions for Python. What’s more, with the addition of support for integration with IPython/Jupyter notebooks, code compiled with Cython can be used in Jupyter notebooks via inline annotations just like any other Python code.

Take your pick

Whether you are a scientist, a developer or, simply, a data enthusiast, these tools provide features that can cover your every need. I am certain some of you will not agree with the list above but then again, this is my top 10 list!

Eirini-Eleni Papadopoulou is the editor for JAXenter.com. Coming from an academic background in East Asian Studies, she decided that it was time to go back to her high-school hobby that was computer science and she dived into the development world. Other hobbies include esports and League of Legends, although she never managed to escape elo hell (yet), and she is a guest writer/analyst for competitive LoL at TGH.